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Nerf is a statistical named entity recognition (NER) tool based on linear-chain conditional random fields (CRFs). Nerf is a statistical named entity recognition tool based on linear-chain conditional random fields.

Nerf

Nerf is a statistical named entity recognition tool based on linear-chain conditional random fields.

Principal developer: Jakub Waszczuk
License: 2-clause BSD

Documentation

See the README file from the development repository.

Downloads

Nerf is available in a form of a software distribution which can be downloaded from Hackage using the Cabal tool. To compile Concraft you will also need the Glasgow Haskell Compiler (GHC). The simplest way to get both Cabal and GHC is to install the Haskell Platform. Please see the documentation for more information about the installation process.

Pre-trained model

A model for the Polish language has been trained on the manually annotated subcorpus of the National Corpus of Polish (NCP). It can be used to recognize embedded structures of named entities consistent with the type hierarchy used in NCP.

Python version

The Python version of Nerf is no longer supported.

Authors: Jakub Waszczuk, Michał Lenart
License: GPL v.3

Readme file of the Python version, in English NERF.pdf

You can download the obsolete distribution package from here. The package consists of three components:

  • Python pycrf library, which has to be installed before the Nerf tool can be used,
  • The Nerf tool itself,
  • Supplementary data: trained models and examples of configuration files.